Advanced Guide to Serverless Data Warehousing on AWS
Get a deep dive into Serverless Data Warehousing in AWS, its design patterns and explore what its future holds.
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Data warehouses have an older design, which becomes stifling in a world where information and data escalate at an exponential pace. Just try to picture hundreds of hours dedicated to managing infrastructure, fine-tuning the clusters to address the workload variance, and dealing with significant upfront costs before you get a chance to analyze the data.
Unfortunately, this is the best that one can expect out of traditional data warehousing methodologies. For data architects, engineers, and scientists, these burdens become a thorn in their side, reducing innovation by 30% and slowing the process of gaining insights from increasingly large data sets by up to 50%.
Serverless Data Warehousing: A Revolution for the Modern Data Master
But what if there was a better way? Serverless data warehousing is a new concept, and it provides a revolutionary solution away from the chaining constraints that come with managing complex infrastructure. Think about the future, where servers are self-provisioning and can scale up or down based on the load. A world where one pays only for the resources consumed or needed, excluding hefty charges and data investments.
Serverless data warehousing opens up this very possibility. By leveraging the power of the cloud, data engineers or scientists can focus on what truly matters: turning collected information into insights from which organizations can make relevant decisions and gain benefits.
Building a B2B Serverless Data Warehouse on AWS: Recommended Design Patterns
As data architects and engineers, we need to see the importance of proper data pipelines for solid B2B analytics and insights. In this case, serverless data warehousing on AWS remains a suitable solution due to its flexibility and affordability. Now, let us explore the proposed design patterns for creating your B2B serverless data warehousing architecture.
Data Ingestion Pipeline
The building block is to create a proper data ingestion process that feeds into the ‘real-time’ layer. Here, the AWS Kinesis Firehose stands out. It is a fully managed service that can integrate streaming data in real-time from B2B sources like your CRM or ERP system. Firehose consumes the data and directs it to storage layer S3, which is a low-cost storage layer for storing raw and processed data.
Data Transformation and Orchestration
In most cases, transformations are made when extracting value from raw data. Enter AWS Glue as the serverless ETL (extract, transform, load) solution. Glue allows you to fulfill data transformations with Python scripts and, at the same time, manage all the stages of data ingestion. This helps in the proper flow of data from B2B sources to the data warehouse without any hitches.
Data Storage and Catalog
Amazon S3 can be considered the foundation of your data store or data lake. This fast-scaled-out object storage service is an economical solution to store all the B2B data, both in its raw and transformed forms. Also, manage and use the AWS Glue Data Catalog effectively. This centralized metadata repository reduces the problem of finding your data by making data search easy by presenting a list of the data stored in S3 in a catalog.
To Know More, Read Full Article @ https://ai-techpark.com/serverless-data-warehousing-in-aws/
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